As our client’s management team grappled with these challenges, they turned to us for answers. Linedata experts ran a series of workshops with the client’s leadership team , including the COO and Heads of Innovation, Data Management and Middle Office Accounting.
We discussed their pain points and business objectives and devised a solution built around machine learning models that would identify patterns of behavior that they had not recognized previously as causes and precursors to operational failure. Together, we designed a cost-effective, future-proof solution reflecting the client’s forward-thinking approach to technology as an enabler of growth.
Data-driven insights, powered by machine learning
Many companies can write powerful machine learning algorithms, but few possess Linedata’s deep knowledge of asset management operations. Our project team of systems and operational experts and data scientists drew on Linedata’s 20-plus years as a global provider of asset management solutions as they set out to determine what was causing trade failures: internal process breakdowns, data feed issues, or other unknown causes? The resulting machine learning models consumed the client’s trade and operational data, analyzing combinations of factors including asset types, trade venues and input sources to determine which trades were most likely to require amendment. The models also looked at the relative reliability of price types from different sources, and whether trades at certain times of the month or year were more error prone.
By evaluating data over time and running hundreds of scenarios, the models were able to predict exceptionally busy days, enabling the firm to ensure correct staffing and appropriate planning. Potentially risky trades and associated operational and data processes were also highlighted for process improvements.
The actionable insights provided forward-looking scenarios where certain transaction types might have high failure rates based on prior experience. And, by focusing on why trade failures occurred, Linedata Analytics Service helped the firm make changes to improve operational outcomes.
Driving continuous improvement
Implementing the insights provided by Linedata has enabled our client to significantly reduce its trade amendments and achieve process improvements and related cost and efficiency benefits. But such initial gains are far from a one-off exercise. The Linedata Analytics Service models continually ‘get smarter’ in line with the evolving business and operational context, driving a virtuous continuous improvement circle. Clients can further fine-tune their operations, mitigate operational and reputational risk, and lower their cost curves, making Linedata Analytics Service a‘ virtual partner’ in achieving their business objectives.
“Clients trust us to solve their operational analytics challenges because of Linedata’s combination of access to the right data, machine learning expertise and 20 years of operational experience with asset managers.”
Philitsa Hanson, head of transformation, Linedata